Establish a Data Fabric to Enable a Path for True Digital Transformation

Data is an integral element of digital transformation for enterprises. But as organizations seek to leverage their data, they encounter challenges resulting from diverse data sources, types, structures, environments, and platforms. This multidimensional data predicament is further complicated when organizations adopt hybrid and multi-cloud architectures.

For many enterprises today, operational data has largely remained siloed and hidden, leading to an enormous amount of dark data. Many life sciences organizations have sought to reinvent themselves as a data-driven organization—to become a company where data science capabilities were readily accessible across multiple business units. They soon realized that their digital transformation was hampered by siloed data, inconsistent tools, and various skill levels, all of which caused critical gaps between data competencies. The problem they faced was not unique to their business; in fact, it is a common consequence of data landscapes that have outgrown their data management architectures.

In order to get on a successful path for digital transformation, many progressive life sciences companies are employing a new data architecture concept known as data fabric. In the past, organizations have attempted to address data access problems either through point-to-point integration or introduction of data hubs. Neither of those are suitable when data is highly distributed and siloed. Point-to-point integrations add exponential cost for any additional end point that needs to be connected, meaning this is a non-scalable approach. Data hubs allow for easier integration of applications and sources but exacerbate the cost and complexity to maintain quality and trust of data within the hub.

The data fabric is an emerging architecture that aims to address the data challenges arising out of a hybrid data landscape. Its fundamental idea is to strike a balance between decentralization and globalization by acting as the virtual connective tissue between data endpoints. Through technologies such as automation and augmentation of integration, federated governance, as well as activation of metadata, a data fabric architecture enables dynamic and intelligent data orchestration across a distributed landscape, creating a network of instantly available information to power a business.

A data fabric is agnostic to deployment platforms, data processes, geographical locations, and architectural approach. It facilitates the use of data as an enterprise asset. A data fabric ensures your various kinds of data can be successfully combined, accessed, and governed both efficiently and effectively.

The Four Capabilities of Data Fabric

A data fabric could be logically divided into four capabilities (or components):

1. Knowledge, insights, and semantics

  • Provides a data marketplace and shopping experience.
  • Automatically enriches discovered data assets with knowledge and semantics, allowing consumers to find and understand the data.

2. Unified governance and compliance

  • Allows local management and governance of metadata but supports a global unified view and policy enforcement.
  • Automatically applies policies on data assets in accordance with global and local rules.
  • Utilizes advanced capabilities to automate data asset classification and curation.
  • Automatically establishes queryable access routes for any cataloged assets for increased activation of data.

3. Intelligent integration

  • Accelerates a data engineer’s tasks through automated flow and pipeline creation across distributed data sources.
  • Enables self-service ingestion and data access over any data with local and global deep enforcement of data protection policies.
  • Automatically determines best fit execution through optimized workload distribution and self-tuning and correction of schema drifts.

4. Orchestration and lifecycle

  • Enables the composition, testing, operation, and monitoring of data pipelines.
  • Infuses AI capabilities in the data lifecycle to automate tasks, self-tune, self-heal, and detect source data changes, all of which facilitates automated updates.

Business Benefits of a Data Fabric

Data only delivers business value when it is contextualized and becomes accessible by any user or application in the organization. When implemented correctly, a data fabric helps ensure those values are available throughout the organization in the most efficient and automated way possible. As such, the fabric has three key benefits:

1. Enable self-service data consumption and collaboration

By integrating data from multiple sources and analyzing a larger fraction of the enormous amount of data generated daily, organizations gain better insights and respond more quickly to changing business demands. A data fabric rapidly delivers data into the hands of those who need it. Self-service enables the organization as a whole to find appropriate data quicker and spend more time using that data to provide tangible insights.

Benefits of data fabric for self-service data consumption:

  • Business users have a single point of access to find, understand, shape, and consume data throughout the organization.
  • A centralized data governance and lineage help users understand what the data means, where it comes from, and how it is related to other assets.
  • Extensive and customizable metadata management scales easily and is accessible via APIs.
  • Self-service access to trusted and governed data enables line-of-business collaboration with other users

2. Automate governance, protection, and security; enabled by active metadata

A distributed active governance layer for all data initiatives reduces compliance and regulatory risks by providing trust and transparency. It enables automatic policy enforcement for any data access, providing a high level of data protection and compliance. Utilizing AI and machine learning technologies allows data fabric users to increase their level of automation, for example automatically extracting data governance rules based on language and definitions in regulatory documents. This allows organizations to apply industry-specific governance rules in a matter of minutes to help avoid costly fines and ensure ethical use of data wherever it resides.

Benefits of a data fabric for governed virtualization:

  • Agility, security, and productivity is increased for data engineers, data scientists, and business analysts.
  • Multiple global data sources appear as one database.
  • New, industry-leading discovery of personally identifiable information (PII) and critical data elements is possible at massive scale.

3. Automate data engineering tasks and augment data integration across hybrid cloud resources

Advanced data engineering means that virtually any data access or delivery process is automated and not requiring any tedious or error-prone coding process. Augmentation of integration utilizes metadata data to optimize the data delivery and access.

Benefits of a data fabric for data engineering and integration:

  • Automatically optimized data integration helps accelerate data delivery.
  • Automatic workload balancing, and elastic scaling means jobs are ready for any environment and any data volume.
  • Resiliency and CI/CD automation are built in.
  • The automated process for capturing changes in real time supports delivery of quality data for business processes.
  • Machine learning can automate and extend custom data discovery, classification, and curation processes, leading to faster time-to-value.
  • Continuous analysis can be automatically performed in real time, wherever data lives.

This article is based on IBM’s white paper on Data Fabric approach.


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